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The dataset is read from a XRFF file (IN XRFF).
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Statistical errors for the background with bkg_id=1 of the default dataset are read from the first two columns (the default) of the ASCII file staterror.txt.
Systematic errors for the background with bkg_id=1 of the default dataset are read from the first two columns (the default) of the ASCII file syserror.dat.
The R and S datasets are read as plain text from HDFS and delivered to two separate concurrent flatMap transformations, identifiable by the input source file.
Regarding the performed FDG-PET/CT studies, the acquired functional and morphological datasets were read separately.
Using source-partition images for both datasets, all SSFP MRA datasets were read at the first session, followed by all CE-MRA datasets at the second session.
Study population datasets are read into SAS macros which select patients and enroll them into a study based on the specific design criteria if the study is open to enrollment.
The dataset can be read into R using command read.table.table
The value of is specific to a dataset, so it is read from the file that contains the projection data.
The relatively low number of junctions found overall despite the size of the dataset is likely a result of low read quality.
Notice that all of the datasets can be read directly by val3dity, i.e. without any format conversion necessary as a pre-processing.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com